White matter microstructural abnormalities and default network degeneration are associated with early memory deficit in Alzheimer’s disease continuum

Instead of assuming a constant relationship between brain abnormalities and memory impairment, we aimed to examine the stage-dependent contributions of multimodal brain structural and functional deterioration to memory impairment in the Alzheimer’s disease (AD) continuum. We assessed grey matter volume, white matter (WM) microstructural measures (free-water (FW) and FW-corrected fractional anisotropy), and functional connectivity of the default mode network (DMN) in 54 amnestic mild cognitive impairment (aMCI) and 46 AD. We employed a novel sparse varying coefficient model to investigate how the associations between abnormal brain measures and memory impairment varied throughout disease continuum. We found lower functional connectivity in the DMN was related to worse memory across AD continuum. Higher widespread white matter FW and lower fractional anisotropy in the fornix showed a stronger association with memory impairment in the early aMCI stage; such WM-memory associations then decreased with increased dementia severity. Notably, the effect of the DMN atrophy occurred in early aMCI stage, while the effect of the medial temporal atrophy occurred in the AD stage. Our study provided evidence to support the hypothetical progression models underlying memory dysfunction in AD cascade and underscored the importance of FW increases and DMN degeneration in early stage of memory deficit.


Results
Specific brain structural and functional abnormalities are associated with memory deficit. To determine regions-of-interests for SVC modelling, we performed several whole-brain voxel-wise analyses on the associations between brain abnormalities and memory deficit in patients. The whole-brain voxel-wise analysis on the FW-corrected diffusion MRI metrics showed that lower memory scores in aMCI and AD patients were associated with higher FW in most WM regions. (Fig. 1A, Supplementary Table 1). In contrast, lower memory score was associated with lower fractional anisotropy (FA T ) in the body of the fornix only (Fig. 1B, Supplementary Table 1).
The voxel-wise analysis on grey matter volume revealed that lower GMV in the bilateral MTL (particularly in the HIP), PCC, and mPFC were associated with lower memory scores across all patients ( Fig. 2A, Supplementary  Table 2).
Finally, the voxel-wise analysis on the DMN FC revealed that lower memory score was associated with lower FC in the precuneus and part of PCC regions across all patients (Fig. 2B, Supplementary Table 3).
These findings remained significant after controlling for years of education. Further details are provided in Supplemental Data ( Supplementary Fig. 5, Supplementary Results).
In addition, we found greater brain abnormities (FW, FA T , GMV and FC) in AD patients compared with aMCI patients as expected ( Supplementary Fig. 2), which included those memory-related brain measures. Further details for group difference results among HC, aMCI and AD are provided in Supplemental Data (Supplementary Results). Whole-brain voxel-wise linear regression analysis indicated that higher FW values in widespread brain regions were associated with poorer memory. (B) Lower FA T in the body of the fornix was associated with worse memory. The WM skeleton is highlighted in green. All the results are threshold-free cluster enhancement and family-wise error-corrected at p < 0.05. severity) contributions of both brain functional and structural measurements simultaneously, we built an SVC model with memory as the dependent variable and FW, FA T in the fornix, GMV-mPFC, GMV-PCC, GMV-HIP, and FC-DMN derived from the significant regions of voxel-wise analysis as predictors.
We found these brain measures exhibited differential severity-dependent associations with memory ( Fig. 3). For DTI, FW had the greatest influence on memory deficit in the early aMCI phase where higher FW was associated with lower memory score (peak beta = −0.9). However, this influence gradually decreased in late aMCI and AD stage (i.e., less negative betas approaching zero). Similarly, the association of FA T in the fornix with memory score was the greatest in early aMCI stage (peak beta = 4.5), where higher FA T was associated with better memory score. However, this association quickly diminished in the AD stage (i.e., smaller positive betas approaching zero).
For GMV, both PCC and mPFC had the strongest associations with memory in the early aMCI stage, where larger volume was associated with better memory score (mPFC peak beta = 4.5; PCC peak beta = 1.4). Similar to FA T , this relationship gradually diminished in the AD stage (i.e., smaller positive betas). In contrast, the relationship between hippocampus (and MTL) and memory were more evident in the late aMCI stage and peaked at the early AD phase (beta = 2.4) where larger volume was associated with better memory (i.e., greater positive betas). The association between FC-DMN and memory was evident throughout the disease continuum. Higher FC was associated with higher memory score regardless of severity (i.e., comparable positive betas).
We also evaluated the specificity of SVC model following our previous approach 32 . We randomly permuted the memory scores 100 times across the subjects and repeated SVC modelling 100 times on each of the 100 permuted data sets (dependent variable: memory z-scores; 10 predictors: brain measures [FW, FA T , FC-DMN, GMV-PCC, GMV-mPFC, GMV-HIP] together with nuisance variables [age, gender, handedness and ethnicity]). In 52 out of the 100 permuted data sets, no variable was selected by all 100 repetitions. For each of the remaining 48 permutated datasets, the SVC model selected one variable from 10 predictors as the key predictor of verbal memory scores based on 100 repetitions. However, the frequency distribution of variable selection across these 48 data sets was random. None of the predictors was selected for all 100 repetitions ( Supplementary Fig. 3). Overall, the selected variables using our original data set did not favour other variables in the null distribution. This indicates the high specificity of SVC models built on the original dataset.
Lastly, when the years of education was added into the SVC modelling as a covariate, the estimated severity-dependent relationships of all brain regions with memory remained similar as the SVC model without education (Supplementary Results and Supplementary Fig. 6).

Discussion
The present study demonstrated differential stage-dependent associations between brain structural/functional abnormalities and memory impairment over the course of AD progression using SVC model. Our findings support the hypothetical model of sequential but temporally overlapping multimodal brain abnormality cascades in AD 11,[18][19][20][21] . A key advantage of SVC model 32 is the use of one multivariant model to compare the stage-varying influences of FW increases, fornix degeneration, GM atrophy of MTL and DMN hubs, and DMN dysfunction on memory deficit as AD progresses. This model does not require the assumption of constant brain-cognition relationships over disease progression; instead, it captures the nonlinear trajectories of these relationships. Specifically, lower FA T and higher FW had stronger associations with memory deficit in patients at early aMCI stage (in contrast to AD stage). Similarly, atrophy in the DMN (mPFC and PCC) was more strongly associated with memory deficit in patients with aMCI. In contrast, GMV loss in the MTL was more strongly associated with poor memory in the AD phase. Compared to the stage dependence in the structural measures, an association between DMN functional disconnections and memory impairment persisted throughout AD progression. Our findings provide new insight into the multifaceted neurobiological mechanisms underlying memory dysfunction along the AD continuum and highlight the potential importance of WM microstructural abnormalities and DMN degeneration in early cognitive deterioration. Multimodal neuroimaging assays could be further developed to track the efficacy of early cognitive intervention strategies. www.nature.com/scientificreports www.nature.com/scientificreports/ Consistent with the hypothesis that early WM dysfunction appears in the early stage of AD 11 , our results demonstrated that microstructural WM measures were associated with memory performance in the aMCI phase. Importantly, by applying the free-water imaging method, we further demonstrated that two different WM pathophysiological measures were associated with memory deficit in the aMCI stage: higher global FW and focal tissue damage in the body of the fornix. Previous studies demonstrated widespread FW increases in AD and aMCI subjects as compared with HC subjects 15,33 . This water content increase may be due to microvascular degeneration 11,34 and neuroinflammation-related modulation of the blood-brain barrier permeability 35 in the widespread WM tissues of AD patients. However, the functional significance of such an increase in FW is not well understood. In this study, we showed widespread increase in FW was associated with memory deficit, particularly in the aMCI stage. This suggests that widespread small vascular degeneration and/or chronic neuroinflammation might play important roles in memory deficit during the early stage of AD 36 . Additionally, we observed a slight 'bump' of the FW-memory association in the clinical phase (relatively more negative betas at the AD stage in Fig. 3). These two phases of stronger FW-memory association during disease progression are in line with outcomes from a recent study where both early and late peaks of microglial activation (which triggers inflammation) were involved in prodromal and clinical stages of AD 27 , respectively. Therefore, our results might suggest the potential role of FW increases in memory deficit in the early aMCI stage.
Another important finding in our study is that the FA T in the body of the fornix is associated with memory deficit. Our SVC model showed that this association peaked at aMCI and then decreased during the AD stage. The fornix is a predominant tract connecting the hippocampus to the septal nuclei and the mammillary bodies in the hypothalamus. It is particularly susceptible to pathological assaults and shows early changes in AD 37 . Moreover, the fornix microstructure has been used to classify AD diagnosis and assess cognitive changes and response to therapy in both human 13 and animal models 38 . Recent studies have demonstrated that fornix microstructure accounts for both age-related and age-independent variations in free recall test 39 . A prior longitudinal study also indicated that FA in the fornix could predict memory decline and progression to AD in MCI patients 12 . Of note, this focal fornix tissue damage had greater association (in term of beta) with memory deficit than the global FW increase, which suggests that memory-related WM tract deterioration may play a more dominant role than the widespread 'background' vascular/inflammatory damage in memory performance decline. Therefore, our SVC results further bolstered the plausibility that the fornix may be one of the earliest damaged regions that potentially contribute to worse memory outcome in AD.
In contrast to the stronger influence of hippocampal atrophy in AD stage, we found atrophy in the DMN hubs (mPFC and PCC) to be more strongly associated with poorer memory performance in the aMCI phase. Past studies have reported both MTL atrophy and DMN damage occurs at the early stage of AD 6,40 . However, the stage-dependant contribution of these GM regions to memory deficits remains unknown. Using the multivariant SVC model that combined mPFC, PCC and MTL regions, our findings provide evidence that DMN atrophy may have greater influence to memory decline at the aMCI stage, while MTL atrophy has greater contribution at AD stage. Furthermore, studies have demonstrated that GM atrophy mediates the effects of amyloid and Tau on memory 41,42 . Our results on the differential stage-dependent atrophy-memory association are consistent with the pathophysiological mechanisms of AD progression: neuronal degeneration in the DMN related to early amyloid burden and hypometabolism and medial temporal atrophy related to later Tau pathology in the clinical stage of AD 7,18,43 . Both hippocampus and DMN hubs functionally support complementary functions in episodic memory. The hippocampus organizes memories in the context in which they were experienced (a defining feature of episodic memory), whereas the DMN hubs control the retrieval of memories by suppressing competing memories and are responsible for flexibly switching between memory 'tracks' according to contextual rules 9,44 . Indeed, interference suppression and retrieval processes have been compromised in healthy elderly and patients with aMCI 45,46 , consistent with the observation of an early stronger GMV-memory association in the DMN than in the MTL. Additionally, we observed that mPFC had slightly higher association (in term of beta) with memory than the PCC at the early aMCI stage, which was consistent with previous literature that prefrontal cortex plays an essential role in the memory processing pathway 9 .
In contrast to the structural measures, the FC of the DMN hubs showed positive associations with memory performance across both prodromal and clinical AD stages. These findings are consistent with prior studies 5,19 . Synaptic dysfunction and grey and white matter deteriorations could impact the functional organization of the DMN and lead to memory deficit 7,10,47 . As a result, the association between PCC-based DMN FC and memory remained relatively stable across disease progression.
Overall, our results suggest a possible mechanism of memory deficit in AD. During the early stage of AD, the structure and function of the DMN hubs (particularly PCC and mPFC) may be targeted due to selective vulnerability 48 and/or early amyloid burden 43 , accompanied by the associated WM deterioration, disconnection with hippocampus, and widespread WM inflammation and vascular damage 15,35,40 . Taken together, these factors may impair memory performance. As AD progresses, the impacts of WM damages to memory would be greatly reduced due to possible ceiling effects 11 . Along with this process, MTL atrophy and more severe functional network breakdown become the dominant factors contributing to further memory impairments 6,18 , supporting the hypothetical AD cascade model 11,18,19 . Therefore, our results implied that extracellular FW increases and DMN degeneration may be the potential targets for early intervention strategies to slow down memory decline in AD, while MTL atrophy in late AD may be used as an imaging marker to monitor progression of memory deficit 18 .
Although we have demonstrated the significance of stage-dependent contributions of multimodal brain structural and functional deterioration to memory impairment in AD progression, our study has limitations. One limitation is that the associations between brain function/structure and memory derived from the cross-sectional dataset may be confounded by inter-subject anatomical variability and not fully reflect within-subject longitudinal stage-dependent brain-cognition associations. However, our findings are consistent with the AD cascade hypothesis and can serve as a working model for future longitudinal studies. Secondly, no amyloid PET imaging www.nature.com/scientificreports www.nature.com/scientificreports/ or cerebrospinal fluid markers were available for this cohort. Therefore, we could not rule out the possibility of other pathologies besides AD in patients with aMCI and AD. Thirdly, although we used global signal regression to remove physiological noise, residuals of physiological signals could still remain 49,50 . Advanced methods such as RETROICOR 51 making use of concurrent physiological recordings are needed in the future to mitigate the influence of physiological noise. Fourthly, there was a relatively limited sample size of participants in those bins with severe dementia symptom (CDR-SB > 10), leading to non-uniform CDR-SB distribution ( Supplementary  Fig. 4), which might affect the estimation accuracy in the SVC modelling at the end of the dementia spectrum. Future studies on larger sample with longitudinal follow-ups would help characterize finer severity-dependent brain-cognition trajectories. Furthermore, the initial screening step of linear regression might miss some brain regions whose structural or functional properties influence memory in a non-linear manner, which require complex statistical modelling to infer nonlinear stage-dependent brain-behaviour relationship 28 . Lastly, compared to the current single shell diffusion MRI data, advanced FW correction based on multi-shell data would further improve the accuracy of FW separation 16 .

Conclusion
Based on the sequential but temporally overlapping patterns of brain-memory associations, our study supports the hypothetical progression models of multimodality brain integrity related to memory dysfunction in the AD continuum. Furthermore, our results underscore the importance of WM microstructure, extracellular water, and DMN degeneration in the early stage of the disease, which may guide treatment options to slow down cognitive decline.

Methods
Ethics approval and consent to participate. This study was conducted in accordance with the Declaration of Helsinki, and written informed consent was obtained from each participant. Ethical approval was provided by the National Healthcare Group Domain-Specific Review Board, Singapore. Both aMCI and AD diagnoses were made at weekly consensus meetings in which clinical features, blood tests, psychometrics, and neuroimaging data were reviewed 52 . Computed tomography (CT), magnetic resonance imaging (MRI), and magnetic resonance angiography were reviewed as part of the diagnostic process. Clinical AD was diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders IV criteria (DMS-IV) and the National Institute of Neurological and Communicative Disorders and Stroke and the AD and Related Disorders Association guidelines for AD 52 . AD patients had a gradual and slow onset of memory problems, impairment in objective neuropsychological assessment, and loss of activities of daily living. All the AD patients had CDR global ≥1 and CDR sum of box ≥4. Clinical aMCI was diagnosed based on: (i) subjective complaints of memory loss, (ii) memory (verbal or visual) impairment on neuropsychological assessment described above, and (iii) absence of diagnosed dementia based on the DSM-IV criteria 53,54 . All the aMCI patients had CDR global <1 and CDR sum of box <4. We excluded participants with significant cerebrovascular disease or psychiatric/neurologic disorders 55 (see details in Supplementary). For the healthy controls (HC), we ensured that the participants had no impairment in the seven domains, their MMSE scores were greater than or equal to 26, and CDR were equal to 0 56,57 .

Participants. All patients
Of the 172 eligible HC, aMCI and AD subjects who were selected between August 12, 2010, and June 22, 2016, 5 participants did not have full MRI scans; 16 participants did not pass quality control criteria for structural MRI, resting-state functional MRI, or DTI (see quality control criteria in supplementary); and 5 participants did not complete the neuropsychological assessments. The remaining 151 participants (51 HC, 54 aMCI, 46 AD) were included in the analyses (Table 1) www.nature.com/scientificreports www.nature.com/scientificreports/ Diffusion MRI data pre-processing. The diffusion MRI data were pre-processed using FSL (http://www. fmrib.ox.ac.uk/fsl) 32 . Head movements and eddy current distortions were corrected to the first b = 0 volume via affine registration of the diffusion-weighted images. Data were discarded if the maximum displacement relative to the first b = 0 volume was greater than 3 mm. The diffusion gradients were rotated to compensate for the registration. Individual maps were visually inspected for signal dropout, artefacts, and additional motion. Individual fractional anisotropy (FA) maps were created by fitting the DTI model to the pre-processed diffusion data at each voxel. FA images were non-linearly registered to the high-resolution (1 mm 3 ) FMRIB58 FA image and then skeletonized using TBSS for further statistical analysis.
Free-water imaging method. We employed the free-water imaging method on the pre-processed diffusion MRI data to estimate the fractional volume of freely diffusing extracellular water molecules (FW) and the fractional anisotropy of water molecules in the proximity of tissue (FA T ) 14,15 . Briefly, the FW compartment models water molecules that are free to diffuse and not restricted or hindered during the diffusion process. This compartment has a fixed diffusivity of 3 × 10 −3 mm 2 /s (the diffusion coefficient of free-water at body temperature), and the fractional volume of this compartment in each voxel forms the FW map. The FW-corrected DTI compartment models water molecules in the proximity of cellular membranes of brain tissue using a diffusion tensor, from which the FA T measure is derived. Therefore, the FW-corrected DTI compartment is corrected for contamination with freely diffusing extracellular water and is consequently expected to be more sensitive and specific to axonal changes than the measures derived from the single tensor model 33 . Voxel-wise FW and FA T were obtained for each subject 16 . The aligned FW and FA T maps of each participant were then projected onto the standardized FA skeleton, resulting in subject-level skeletonized images.
Voxel-based morphometry. We applied optimized voxel-based morphometry (Computational Anatomy Toolbox 12) using Statistical Parametric Mapping (SPM12) 55 . Briefly, we derived the subject-level GMV probability maps from the T1 structural images using an approach that included: (1) segmentation of individual T1-weighted images into the GM, WM and CSF; (2) creation of a study-specific template using non-linear DARTEL (Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra) registration of the affine-registered GM and WM segments; (3) registration of each GM/WM probability map to the study-specific template in Montreal Neurological Institute (MNI) space; (4) modulation by multiplying the voxel values by the Jacobian determinants to account for individual brain volumes; and (5) smoothing of the normalized GM maps by a 8-mm isotropic Gaussian kernel.
Functional image pre-processing. Task-free functional MRI images were pre-processed using the Analysis of Functional NeuroImages software (https://afni.nimh.nih.gov/) and FSL 55 . The pre-processing steps included: (1) removal of the first five volumes to allow for magnetic field stabilization; (2) motion correction; (3) time series de-spiking; (4) spatial smoothing; (5) grand mean scaling; (6) band pass temporal filtering; (7) removal of linear and quadratic trends; (8) co-registration of T1 images using boundary-based registration and subsequent registration of the functional images into an MNI-152 space using a non-linear registration tool (FNIRT); and (9) regression of nine nuisance signals (WM, CSF, global signals and six motion parameters) from the pre-processed functional images. To determine whether global signal regression was preferred, we calculated www.nature.com/scientificreports www.nature.com/scientificreports/ the global negative index for each subject, taken as the percentage of voxels showing a negative correlation with the global signal 55 . Majority of our subjects (90.1%) had the global negative index of <3%, suggesting that the global signal was more representative of non-neural noise and should be regressed out from the images.

Functional connectivity analyses.
Individual-level DMN functional connectivity maps were obtained using a seed-based approach with the REST toolbox 58 . We created spherical region of interest (ROIs) with a 4-mm radius centred at the left posterior cingulate cortex (MNI coordinates [−7, −43, 33]). This seed was previously determined as a core region of DMN 48,59 . Pearson's correlations were then computed between the time-series of every voxel in the brain and the average time series of the seed ROI. The FC correlation maps were converted to z-score maps using Fisher's r-to-z transformation.
Statistical analyses. We analysed the demographic, clinical, and cognitive measures across groups via ANOVA or χ 2 tests using Statistical Package for Social Sciences (SPSS v. 23.0) software. The results were reported at a significance level of p < 0.05.
Associations between brain structure/functional measures and memory impairment. At the first step, to identify region-specific WM changes underlying memory deficit in patients, we built voxel-wise general linear models (GLMs) with the skeletonized FW and FA T images as the dependent variables separately using the FSL. In each model, the memory domain z-score was the independent variable of interest, with age, gender, handedness and ethnicity as covariates. Regions were examined for statistical significance using threshold-free cluster enhancement (TFCE) and permutation-based non-parametric testing (FSL Randomise). Results were family-wise error (FWE) corrected at p < 0.05.
To examine the association between GMV and memory function among the aMCI and AD patients, we built the voxel-wise GLMs using SPM12 toolbox, with a threshold at p < 0.05, FWE corrected. To examine whether and how FC within the DMN related to memory performance across the aMCI and AD patients, we built voxel-wise GLMs using the REST toolbox 58 . Analysis was restricted to the DMN based a predefined group-level mask derived from an independent group of healthy control subjects 55 . The results were reported at a height threshold of p < 0.01 and a cluster threshold of p < 0.05 with Gaussian random field (GRF) correction 58 . We then extracted the mean values of brain structural/functional measures from the resulting significant regions for further statistical analyses.
Sparse varying coefficient (SVC) modelling of severity-dependent associations between brain measures and memory impairment. In reality, the differential pathophysiologies in GM and WM might interact with each other to influence with memory in AD 18 . Furthermore, there are no firm boundaries between the various clinical stages 1 . Therefore, in the second step, we employed the SVC model 31,32 to integrate all structural and functional measures derived from the previous screening step as predictors in the same model to evaluate their relative contribution to and severity-dependent (CDR sum-of-boxes (CDR-SB) as a measure of dementia severity) impact on memory, which provides a more comprehensive and nuanced picture. Specifically, we tested whether and how the associations of brain function/structures with memory were dependent on dementia severity using memory z-scores as the dependent variable: where y i (t k ) represents the memory z-scores for subject i(i = 1, 2, …, n) at the dementia severity t k , measured by CDR-SB. x ij (t k ) is the j th (j = 1, 2, …, p) predictor of subject i at CDR-SB t k . β j (t k ) is the estimated coefficient function depending on CDR-SB t k for each predictor. ε i (t k ) represents the independent and identically distributed random errors at t k .
For predictors x ij (t k ), we extracted the mean values from the previous identified candidate regions of interest (i.e., FW, FA T , FC-DMN, and GMV from mPFC, PCC, hippocampus (HIP)). All predictors were put in the same model with age, gender, handedness, and ethnicity included as nuisance variables. Each predictor was standardized to have zero mean and equal variance across observations. To simultaneously achieve regression model fitting and predictor variable selection, we applied the least absolute shrinkage and selection operator (LASSO) 60 to estimate β j (t k ) by minimizing the following penalized least squares function. where λ is the sparsity penalty tuning parameter chosen by a five-fold cross-validation method. The LASSO algorithm performs variable selection by constraining the sum of the squared magnitudes of the coefficients. SVC modelling with the LASSO algorithm was specifically designed for feature selection problems with small sample sizes 31 . We approximated each coefficient function β j using linear combinations of the B-spline basis (number of basis functions L = 4). Our SVC model offers several advantages over a traditional linear regression model: (i) it does not assume that the association of the brain measures with memory remains constant over disease progression and thus considers each beta coefficient (the association of brain function or structure with memory) as a non-linear function of a continuous variable of dementia severity (i.e., CDR-SB); (ii) feature selection with the LASSO sparsity penalty chooses the most important predictors while eliminating the contributions of the less important predictors; and www.nature.com/scientificreports www.nature.com/scientificreports/ (iii) rather than analysing brain measures in separate models, all variables are entered as predictors in the same multivariate model.
To assess the stability of these beta coefficients, we calculated the means and standard errors of the severity-dependent coefficients estimated from 100 replicates. We reported the brain measures that were selected in all 100 repetitions of SVC modelling. SVC modelling was performed by in-house R scripts based on Daye and colleagues 31 .

Data Availability
The data that support the findings of this study are available from Memory Ageing and Cognition Centre (MACC) but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of MACC.